---
license: mit
tags:
- generated_from_trainer
base_model: Microsoft/Multilingual-MiniLM-L12-H384
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: my-model-MiniLM-Area
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# my-model-MiniLM-Area

This model is a fine-tuned version of [Microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/Microsoft/Multilingual-MiniLM-L12-H384) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2516
- Accuracy: 0.5827
- F1: 0.5228
- Precision: 0.4905
- Recall: 0.5827

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 30
- eval_batch_size: 5
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20

### Training results

| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1     | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|
| 1.8735        | 1.0   | 22   | 1.7614          | 0.3094   | 0.1462 | 0.0957    | 0.3094 |
| 1.7665        | 2.0   | 44   | 1.6764          | 0.4604   | 0.3485 | 0.3194    | 0.4604 |
| 1.683         | 3.0   | 66   | 1.5724          | 0.4964   | 0.3684 | 0.2930    | 0.4964 |
| 1.6038        | 4.0   | 88   | 1.5156          | 0.5108   | 0.3787 | 0.3015    | 0.5108 |
| 1.5742        | 5.0   | 110  | 1.4933          | 0.4820   | 0.3582 | 0.2921    | 0.4820 |
| 1.5197        | 6.0   | 132  | 1.4276          | 0.5252   | 0.3909 | 0.3125    | 0.5252 |
| 1.4965        | 7.0   | 154  | 1.4168          | 0.5180   | 0.3845 | 0.3058    | 0.5180 |
| 1.4305        | 8.0   | 176  | 1.4016          | 0.5396   | 0.4551 | 0.3970    | 0.5396 |
| 1.3606        | 9.0   | 198  | 1.3748          | 0.5108   | 0.3920 | 0.3845    | 0.5108 |
| 1.3442        | 10.0  | 220  | 1.4114          | 0.5324   | 0.4359 | 0.3926    | 0.5324 |
| 1.2944        | 11.0  | 242  | 1.3576          | 0.5540   | 0.4688 | 0.4111    | 0.5540 |
| 1.2317        | 12.0  | 264  | 1.3163          | 0.5468   | 0.4652 | 0.4048    | 0.5468 |
| 1.1677        | 13.0  | 286  | 1.2899          | 0.5612   | 0.4866 | 0.4355    | 0.5612 |
| 1.1331        | 14.0  | 308  | 1.3172          | 0.5683   | 0.5073 | 0.5480    | 0.5683 |
| 1.0908        | 15.0  | 330  | 1.2806          | 0.5468   | 0.4730 | 0.4395    | 0.5468 |
| 1.0785        | 16.0  | 352  | 1.2631          | 0.5540   | 0.4743 | 0.4149    | 0.5540 |
| 1.031         | 17.0  | 374  | 1.2667          | 0.5827   | 0.5159 | 0.4693    | 0.5827 |
| 1.0057        | 18.0  | 396  | 1.2617          | 0.5827   | 0.5224 | 0.5055    | 0.5827 |
| 0.9926        | 19.0  | 418  | 1.2477          | 0.5827   | 0.5223 | 0.4897    | 0.5827 |
| 0.9799        | 20.0  | 440  | 1.2516          | 0.5827   | 0.5228 | 0.4905    | 0.5827 |


### Framework versions

- Transformers 4.41.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1